Scientific Applications of Next-generation Cancer Models

HCMI banner with an organoid
Cindy Kyi, Ph.D.

As a contribution to efforts in precision oncology, the National Cancer Institute’s (NCI) Human Cancer Models Initiative (HCMI) is developing next-generation (next-gen) cancer models, which include 3D organoids, neurospheres, 2D adherent, and conditionally reprogrammed cells. These next-gen models are derived from a variety of cancer types including poor outcome cancers, rare cancers, and cancers from ethnic and racial minorities, as well as pediatric populations. HCMI was founded by the NCI, Cancer Research UK, Hubrecht Organoid Technology, and Wellcome Sanger Institute to develop about 1,000 next-gen cancer models. The next-gen cancer models are annotated with molecular characterization data, as well as clinical data to address challenges of traditional cancer cell lines. The goal of HCMI is to provide the research community with a rich resource of diverse, fully annotated next-gen models (NGCMs) to better study disease biology.

Historically, traditional cancer cell lines have provided a platform to conduct large scale studies, such as investigating molecular regulations of cancer cell growth and progression, identifying genetic and biological markers, and predicting drug sensitivities. The Cancer Cell Line Encyclopedia, a repository of cancer cell lines with associated molecular data and analyses, is a valuable resource of over 1,100 cell lines generated from numerous cancer types. Another resource is the Cancer Therapeutics Response Portal (CTRP) which houses a large dataset of quantitative small-molecule sensitivity data of cancer cell lines. This resource could be used to mine for lineages or mutations, enriched among cell lines, that are sensitive to small-molecules and identify new therapeutic vulnerabilities. The next-gen cancer models aim to address limitations of most available cell lines such as poor or unknown representation of the cellular architecture of the original tumor, heterogeneity of cell types and  genetic drivers of cancer subtypes1.

Next-generation models

The successful culture and expansion of organoids from murine small intestinal tissue paved the way for early organoid models generated from mouse colon and human small intestine and colonic epithelium2. Human intestinal organoids were observed to mimic in vivo cellular differentiation; however, adaptations of the culture media were needed to successfully grow organoids from different tissue types. Sato and colleagues reported optimized cell culture methods utilizing growth factors, Notch protein inhibitors, nicotinamide, and kinase enzyme inhibitors for culturing primary human epithelial cells from small intestine, colon, adenoma, adenocarcinomas, and Barrett’s esophagus. The models were hypothesized to be more representative of the tumor biology than colon cancer cell lines2. The group recently published their protocols for generating next-gen cancer models (NGCMs) from breast normal and tumor tissues3.

Identifying an optimized culture medium for each cancer and tissue type is critical for successfully growing next-gen models which retain their originating tumor characteristics. According to Ince and colleagues, ovarian tumor cell lines grown in standard culture media: “(1) had very low success rate (less than one percent) [sic of being established in culture], (2) had long lag times for the first passage, (3) could only be propagated for up to 15 passages and (4) lacked the phenotype of original tumor”4. The authors developed 25 diverse ovarian cell lines using optimized culture media compositions for each human ovarian cancer subtype. The resulting ovarian cancer cell lines retained the genomic landscape, histopathology, and molecular characteristics of the original tumors from which they were derived. The expression profiles and drug responses of these cell lines were also found to correlate with patient outcomes4.

Applications of next-generation models

Next-gen models have been shown to be excellent research tools to carry out ex vivo experiments as they recapitulate the biology and tissue architecture of primary tumors. A few examples of applications of next-gen models in research include studying disease progression, identifying genomic and molecular drivers of diseases, and screening compounds or small molecules for treatment sensitivity and/or resistance.

  • Studying disease progression in pancreatic cancers can be challenging due to lack of patient-derived models that cover the full spectrum of disease progression, lack of clinical correlations, and accumulation of genetic aberrations. Boj and colleagues were able to generate human derived organoids from normal and neoplastic ductal cells using modified culture conditions5. Through targeted sequencing of cancer-associated genes on organoids derived from human normal and tumor tissues, the authors identified oncogenic KRAS mutations in majority of the tumor-derived models indicating the organoids represented the cancer driver mutations observed in the originating human tumors.
  • Patient-derived organoid models of pancreatic ductal adenocarcinoma (PDAC) were used to test chemosensitivity and chemoresistance of individual tumors. One of the limitations of traditional cell lines is that due to genetic drift after multiple passages, there are differences in genetic profiles between the original tumor and the derived cell lines; such as copy number alterations, DNA methylation, molecular subtypes, and resulting phenotypes. Tiriac and colleagues found that the patient-derived PDAC organoids harbored genetic alterations that are consistent with known pathogenic mutations in PDAC6. The authors concluded that the organoids recapitulated the mutational spectrum and molecular subtypes of primary pancreatic cancer and, therefore, are excellent models to accurately examine and predict responses to chemotherapeutic agents6.
  • The lack of model systems that reflect the pathology of the primary disease and responses to therapy presents a challenge in studying esophageal adenocarcinoma (EAC)1. Using patient tumor-derived organoid models, Li and colleagues could identify tumor drivers of EAC through histological and genomic characterization1. The molecular annotation of the EAC organoids showed that they retained patient-specific gene expression, disrupted cellular polarity, intra-tumor heterogeneity, and drug sensitivity1. Based on these findings, the use of patient-derived organoids provides model systems to accurately study disease.
  • The variability in drug response due to cellular heterogeneity presents another challenge in cancer research using traditional cell lines. Next-gen cancer models were shown to produce reliable responses that resemble those of the originating tumor when screening targeted therapy compounds7.Similar to the responses found in mouse organoids, treatment of patient-derived organoids with Itraconazole, a cell cycle inhibitor compound8, led to inhibited organoid growth and cell death7. Buczacki and colleagues suggested that they found a therapeutic potential of Itraconazole in inducing cancer cell death and preventing late recurrence in colorectal cancer7. Hence, patient-derived organoids could be used to identify novel drug targets.
  • The lack of cellular architecture to mirror the tumor microenvironment and stromal response presents difficulty in studying immune interactions in cancer. It is challenging to elicit tumor-immune specific responses using traditional cell lines without a tumor microenvironment. Studying tumors using patient-derived xenografts (PDXs) in immunocompromised mice also does not reflect the immune interactions of human tumors due to lack of immune response9. To overcome these challenges, Neal and colleagues used patient-derived 3D models from various tumor types using the air-liquid interface (ALI) method. The models included a mixture of cancer cells and several immune cell types, the latter expressing immune check-point surface receptor programmed cell death protein-1(PD-1). These models enabled the study of immune interactions with cancer cells by providing in vitro tumor microenvironment10. To test for anti-PD-1-dependent tumor cell killing, the ALI models were treated with PD-1 blocking antibody. The tumor-infiltrating lymphocytes in the patient-derived models were found to model the immune checkpoint blockade, resulting in cancer cell death10. Hence, the use of NGCMs can aid in mirroring the tumor microenvironment in vitro and provide models for immune-oncology research.

The results summarized above, are just a few examples of important outcomes using the NGCMs.  The models have a great potential in research to improve our understanding of cancer etiology and the improvement of treatment outcomes.  

HCMI next-gen models

The advantages of using HCMI’s NGCMs over traditional cell lines include the availability of clinical data such as patient and tumor information, histopathological biomarkers, and molecular characterization data.  The comprehensive data increase accuracy in identifying driver mutations and targeted therapies in various cancer subtypes. If interested in using the next-gen models developed by HCMI, visit the HCMI Searchable Catalog to query and browse available cancer models based on a subset of clinical and molecular data. Currently, the catalog includes 35 NGCMs that are from the brain, bone, bronchus and lung, colon, pancreas, stomach, and rectum. Standardized protocols and optimized cell culture media formulations for model growth and expansion for each model are available through the third-party distributor, American Type Culture Collection. These HCMI resources should facilitate the repeatability and reproducibility in growing human cancer models specific to each cancer type. The model-associated genomic and clinical data are quality-controlled and harmonized at multiple checkpoints for reliability of associated data, and are available publicly at NCI’s Genomic Data Commons. HCMI next-gen models with associated data provide the research community with a valuable resource to accelerate the translation of research findings to precision oncology.

References

  1. Li X, Francies HE, Secrier M, et al. Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics. Nature Communications. 2018 Jul 30; 9(1):2983. (PMID: 30061675)
  2. Sato T, Stange DE, Ferrante M, et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett's epithelium. Gastroenterology. 2011 Nov;141(5):1762-72. (PMID: 21889923)
  3. Sachs N, de Ligt J, Kopper O, et al. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. Cell. 2018 Jan 11;172(1-2):373-386. (PMID: 29224780)
  4. Ince TA, Sousa AD, Jones MA, et al. Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours. Nature Communications. 2015 Jun 17; 6:7419 (PMID: 26080861)
  5. Boj SF, Hwang CI, Baker LA, et al. Organoid models of human and mouse ductal pancreatic cancer. Cell. 2015 Jan 15;160(1-2):324-38. (PMID: 25557080)
  6. Tiriac H, Belleau P, Engle DD, et al. Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. Cancer Discovery. 2018 Sep;8(9):1112-1129. (PMID: 29853643)
  7. Buczacki SJA, Popova S, Biggs E, et al. Itraconazole targets cell cycle heterogeneity in colorectal cancer. The Journal of Experimental Medicine. 2018 Jul 2;215(7):1891-1912. (PMID: 29853607)
  8. Pantziarka P, Sukhatme V, Bouche G, Meheus L, Sukhatme VP. Repurposing Drugs in Oncology (ReDO)-itraconazole as an anti-cancer agent. E cancer medical science. 2015 Apr 15; 9:521. (PMID: 25932045)
  9. Baker LA, Tiriac H, Clevers H, Tuveson DA. Modeling pancreatic cancer with organoids. Trends in Cancer. 2016 Apr;2(4):176-190. (PMID: 27135056)
  10. Neal JT, Li X, Zhu J, et al. Organoid Modeling of the Tumor Immune Microenvironment. Cell. 2018 Dec 13;175(7):1972-1988. (PMID: 30550791)
Last updated: July 12, 2021